Xu, Y, Schebesch, F, Ravikumar, N et al. (1 more author) (2019) Detection of Unseen Low-Contrast Signals Using Classic and Novel Model Observers. In: Handels, H, Deserno, TM, Maier, A, Maier-Hein, KH, Palm, C and Tolxdorff, T, (eds.) Informatik aktuell. BVM 2019: Bildverarbeitung für die Medizin, 17-19 Mar 2019, Lübeck, Germany. Springer Vieweg , pp. 212-217. ISBN 978-3-658-25325-7
Abstract
Automatic task-based image quality assessment has been of importance in various clinical and research applications. In this paper, we propose a neural network model observer, a novel concept which has recently been investigated. It is trained and tested on simulated images with different contrast levels, with the aim of trying to distinguish images based on their quality/contrast. Our model shows promising properties that its output is sensitive to image contrast, and generalizes well to unseen low-contrast signals. We also compare the results of the proposed approach with those of a channelized hotelling observer (CHO), on the same simulated dataset.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019, Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature. This is a post-peer-review, pre-copyedit version of an article published in Informatik aktuell. The final authenticated version is available online at: https://doi.org/10.1007/978-3-658-25326-4_47. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Aug 2019 10:41 |
Last Modified: | 07 Feb 2020 01:39 |
Status: | Published |
Publisher: | Springer Vieweg |
Identification Number: | 10.1007/978-3-658-25326-4_47 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:149275 |